65 citations · 220 across the 24 of their papers we have counts for
Showing 2021Show all
2 papers · 1 filter
cs.LG2021★ 2 cited
Non-separable Spatio-temporal Graph Kernels via SPDEs
Alexander Nikitin, ST John, Arno Solin +1
Gaussian processes (GPs) provide a principled and direct approach for inference and learning on graphs. However, the lack of justified graph kernels for spatio-temporal modelling h…
stat.ML2021★ 7 cited
GPflux: A Library for Deep Gaussian Processes
Vincent Dutordoir, Hugh Salimbeni, Eric Hambro +7
We introduce GPflux, a Python library for Bayesian deep learning with a strong emphasis on deep Gaussian processes (DGPs). Implementing DGPs is a challenging endeavour due to the v…